Virtual Process Data Linkage of Assembly Stations in High Variance Workshop Production

2017 ◽  
Vol 871 ◽  
pp. 60-68
Author(s):  
Christian Sand ◽  
Dominik Manke ◽  
Jörg Franke
Keyword(s):  
Data Base ◽  
Value Chain ◽  
Data Linkage ◽  
Production Systems ◽  
Process Data ◽  
Local Data ◽  
Serial Production ◽  
Complex Process ◽  
Special Software ◽  
Virtual Process

The advance of digitalization changes the requirements of processes in industrial production and assembly. For this reason, production and assembly must now be able to execute complex process steps. This is about quality and productivity expectations, as well as flexibility and reliability of production, lines and plants [1]. Today, data is generated by almost every system, machine and sensor, yet it is hardly used for process optimization. Manufacturing processes are usually organized as workshop production or chained production systems, in addition to standalone machines [2,3]. Most analytic projects focus on chained systems and serial production, unlike individual machines and specific workshop production. Depending on manufacturing IT, process data from serial production is stored in data bases, which are usually optimized for traceability. Standalone machines and machines within workshop production are scarcely connected to a common data base. The required process data is stored either on the module itself or inside a local data base [4]. The identification of dependencies between individual assembly processes, energy data and the quality of the finished product is necessary for an extended optimization. These optimizations can be process-specific, as well as environmental and resource related. Due to decentralized process data storages, an overall view of a dynamic order-oriented value chain is denied. Therefore, the potential of the machines is largely unused. Based on Data Mining, this advanced development can be counteracted by process monitoring and optimization. Therefore, this paper provides a solution for a virtual process data linkage of assembly stations. This enables the acquisition, processing, transformation and storage of unstructured raw data by special software and methods, which is also able to cope with chained production systems and standalone machines. For further analysis of interdependencies, a visualization is developed for advanced monitoring and optimization [5,6].

Modular Service-Oriented Cyber-Physical Systems for the European Tool Making Industry

2015 ◽  
Vol 752-753 ◽  
pp. 1349-1355 ◽  
Author(s):  
Günther Schuh ◽  
Stefan Rudolf ◽  
Martin Pitsch ◽  
Martin Sommer ◽  
Wilhelm Karmann
Keyword(s):  
Value Chain ◽  
Physical System ◽  
Industrial Product ◽  
Cyber Physical System ◽  
High Wage ◽  
Process Data ◽  
Operating Life ◽  
Serial Production ◽  
Product Service ◽  
Service Oriented

Manufacturing companies in high-wage countries are facing rising challenges in a global market. Increasing customer demands for a higher degree of individualization result in smaller lot sizes and higher variety of products. In addition, competitors from low-wage countries in Asia and Eastern Europe have significantly improved their technical capabilities, resulting in a more competitive environment. The tool making industry provides its customers with the means to achieve excellence in production due to its unique position in the value chain between product development and the serial production of parts. A tool making company’s ability to improve the efficiency of serial production and develop innovative product design is strongly dependent on its capability of integrating itself into the preceding and following customer processes. Over the last years, customer demands for global sourcing of tools have changed from low prices to the demands of extended tool operating life and high operational availability. European tool making companies have learned to take this development as a chance to differentiate themselves from global competitors and subsequently increase their range of services up- and downstream the value chain. As a result, new industrial product-service-systems (IPS²) for the European tool making industry need to be developed that address the demand of a higher degree of integration into the preceding and following customer processes. Within the German Government founded research project “Smart Tools”, an industrial product-service-system (IPS²) for the tool making industry has been developed based on a modular service-oriented cyber-physical system. Core element of the cyber-physical system is the smart tool – an injection molding tool equipped with state-of-the-art sensor technology to capture data on the condition of the tool during its operational use. Its intelligence derives from the condition based interpretation and data management of the collected process data which is also the basis for the design of customer specific services. Besides the successful integration of force and position sensors into the tool, experimental research has delivered important results on the application of solid borne sound sensors for online early detection of tool wear. An innovative concept for the distribution and interpretation of the process data incorporates the specific requirements of the customers. To cope with the demands of individual and small series production in the tool making industry, a modular sensor kit has been developed together with a diagnostic unit for data interpretation and storage of data in an electronic tool book. The developed modular service-oriented cyber-physical system delivers the means to extended tool operating life and improves the overall efficiency of serial production. Based on the results new business models can be developed for tool making companies to differentiate themselves from global competitors and overcome the challenges of production in high-wage countries.

Qualität durch robuste Prozessketten steigern – Potentiale für die Umformtechnik mit Industrie 4.0/Quality improvement through robust process chains – Potentials for metal forming by Industrie 4.0 approaches

2015 ◽  
Vol 105 (10) ◽  
pp. 674-679
Author(s):  
P. Groche ◽  
J. Schreiner ◽  
J. Hohmann ◽  
S. Höhr ◽  
A. Lechler
Keyword(s):  
Value Chain ◽  
Metal Forming ◽  
Deep Understanding ◽  
Industrie 4.0 ◽  
Process Data ◽  
Process Chains ◽  
Recording Analysis ◽  
Robust Process ◽  
And Storage ◽  
Insight Into

Industrie 4.0 gestattet transparente sowie sachgerecht angepasste Wertschöpfungsketten. Dazu ist es nötig, ein tiefgreifendes Prozessverständnis zu besitzen sowie die Aufnahme, Auswertung und Speicherung der relevanten Daten zu bewerkstelligen. Der Beitrag gibt einen Einblick in Industrie 4.0-Ansätze in der Umformtechnik und zeigt ausgewählte Ergebnisse aus dem Verbundprojekt „RobIN 4.0“.   Industrie 4.0 opens the possibility to realize a monitoring and qualified adaption along the entire value chain. Prerequisites for this include a deep understanding of the process as well as achieving the recording, analysis and storage of relevant process data. This paper gives an insight into Industrie 4.0 approaches for the forming industry and presents selected results of the RobIN 4.0-project.

scholarly journals Distributed Fusion of Sensor Data in a Constrained Wireless Network

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1006 ◽  
Author(s):  
Charikleia Papatsimpa ◽  
Jean-Paul Linnartz
Keyword(s):  
Large Scale ◽  
Sensor Nodes ◽  
Sensor Data ◽  
A Posteriori ◽  
Likelihood Ratios ◽  
Process Data ◽  
Local Data ◽  
Fusion Algorithm ◽  
Distributed Information ◽  
Iot Devices

Smart buildings with connected lighting and sensors are likely to become one of the first large-scale applications of the Internet of Things (IoT). However, as the number of interconnected IoT devices is expected to rise exponentially, the amount of collected data will be enormous but highly redundant. Devices will be required to pre-process data locally or at least in their vicinity. Thus, local data fusion, subject to constraint communications will become necessary. In that sense, distributed architectures will become increasingly unavoidable. Anticipating this trend, this paper addresses the problem of presence detection in a building as a distributed sensing of a hidden Markov model (DS-HMM) with limitations on the communication. The key idea in our work is the use of a posteriori probabilities or likelihood ratios (LR) as an appropriate “interface” between heterogeneous sensors with different error profiles. We propose an efficient transmission policy, jointly with a fusion algorithm, to merge data from various HMMs running separately on all sensor nodes but with all the models observing the same Markovian process. To test the feasibility of our DS-HMM concept, a simple proof-of-concept prototype was used in a typical office environment. The experimental results show full functionality and validate the benefits. Our proposed scheme achieved high accuracy while reducing the communication requirements. The concept of DS-HMM and a posteriori probabilities as an interface is suitable for many other applications for distributed information fusion in wireless sensor networks.

Identification for Control of Low Pressure Die Casting

2010 ◽  
Author(s):  
XinMei Shi ◽  
Daan M. Maijer ◽  
Guy Dumont
Keyword(s):  
Heat Transfer ◽  
State Space ◽  
Die Casting ◽  
State Space Model ◽  
Heat Transfer Model ◽  
Transfer Model ◽  
Process Data ◽  
Space Model ◽  
Model Based ◽  
Virtual Process

Controlling and eliminating defects, such as macro-porosity, in die casting processes is an on-going challenge for manufacturers. Current strategies for eliminating defects focus on the execution of a pre-set casting cycle, die structure design or the combination of both. To respond to process variability and mitigate its negative effects, advanced process control methodologies may be employed to dynamically adjust the operational parameters of the process. In this work, a finite element heat transfer model, validated by comparison with experimental data, has been developed to predict the evolution of temperatures and the volume of liquid encapsulation in an experimental casting process. A virtual process, made up of the heat transfer model and a wrapper script for communication, has been employed to simulate the continuous operation of the real process. A stochastic state-space model, based on data from measurements and the virtual process, has been developed to provide a reliable representation of this virtual process. The parameters of the deterministic portion result from system identification of the virtual process, whereas the parameters of the stochastic portion arise from the analysis and comparison of measurement data with virtual process data. The resulting state-space model, which can be extended to a multi-input multi-output model, will facilitate the design of a model-based controller for this process.

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